DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning (AAAI24)

The implementation for DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning (AAAI24)

Requirements

python ==3.6
tensorflow==2.6.2
torch ==1.9.1
torch-cluster ==1.5.9
torch-geometric==2.0.3
torch-scatter ==2.0.7
torch-sparse ==0.6.10
torch-spline-conv ==1.2.1
torchvision ==0.10.0

Data Preparation

We use NAS-Bench-101 and NAS-Bench-201 datasets, both of which are available in open-source projects. The corresponding links can be found at the end of this section, and the relevant configurations refer to the open-source project configuration.

We get the nasbench_only108.tfrecord file of NAS-Bench-101 and NAS-Bench-201-v1_1-096897.pth file of NAS-Bnech-201 in. /dataset.

For the DARTS search space, there are two ways to obtain the labeled dataset, the first one is to start training from scratch, refer to . /train/generate_data.py; the second one is to extract from the training results of NAS-Bench-301. Place the ./darts folder in nasbench301_full_data in . /dataset . Note that we did not use the predicted results of NAS-Bench-301, but only its training results of some architectures on CIFAR-10.

NAS-Bench-101:

project links:https://github.com/google-research/nasbench

dataset links:https://storage.googleapis.com/nasbench/nasbench_full.tfrecord

NAS-Bench-201:

project links:https://github.com/D-X-Y/NAS-Bench-201

dataset links:https://drive.google.com/file/d/16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_/view

DARTS Search space:

project links:https://github.com/automl/nasbench301

dataset links:https://figshare.com/articles/dataset/nasbench301_full_data/13286105

Search

The three .sh files under the folder correspond to searching on three search spaces . The configuration of which can be modified accordingly.

Reference

@inproceedings{zheng2024dclp,
  title={DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning},
  author={Zheng, Shenghe and Wang, Hongzhi and Mu, Tianyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={15},
  pages={17051--17059},
  year={2024}
}